Specific Application Analysis of Human Motion Biomechanics Data in the "Artificial Intelligence+" Environment

With the rapid development of artificial intelligence technology, its application in human motion biomechanics analysis is gradually deepening. Based on the combination of motion capture systems and human biomechanics analysis software, it not only provides important support for sports science and medical rehabilitation but also shows great potential in human-machine interaction design in the industrial intelligent manufacturing field (such as automobile manufacturing and aerospace). This article combines recent domestic and international related research to explore the specific applications of this technology combination in industrial scenarios and its data support methods.

🔥 1  Technical Foundations of Motion Capture and Biomechanics Analysis

Motion capture systems use high-precision sensors (such as optical cameras, inertial measurement units, etc.) to collect real-time human motion data, including joint angles, displacement, velocity, and acceleration parameters. Combined with biomechanics analysis software (such as OpenSim 、 AnyBody 、 BOB etc.), researchers can build computer models of the musculoskeletal system to simulate the mechanical behavior of the human body in different environments. In recent years, the introduction of artificial intelligence algorithms has further improved data processing efficiency and model prediction accuracy. For example, machine learning methods can be used to optimize noise filtering and feature extraction in motion capture, while deep learning can establish nonlinear mapping relationships between high-dimensional data and biomechanical indicators.

🔥 2  Human-Machine Interaction Applications in Industrial Intelligent Manufacturing

2.1  Automobile Manufacturing Field

In the automotive industry, human-machine interaction ( HMI ) design directly affects driving safety and comfort. Through motion capture systems, researchers can obtain real-time motion data of drivers in simulated driving environments (such as upper limb operation range, lower limb pedal pressing force, etc.) and analyze muscle load and joint stress using biomechanical models. This data provides important basis for the design of car seats, steering wheels, and pedals.

For example, in a recent study conducted in cooperation between the Technical University of Munich and BMW Group, researchers used an optical motion capture system and OpenSim software to build a biomechanical model of the driver's upper limbs. By simulating operational behaviors under different sitting postures, they found that the optimized seat design could reduce shoulder muscle load by about 15% , while also reducing fatigue caused by long-term driving. The study further generated personalized seat adjustment schemes through artificial intelligence algorithms, significantly improving driving comfort [1] 。

Additionally, in collision safety research, biomechanical models can simulate the dynamic response of the human body during collisions, providing data support for the design of airbags and seat belts. According to the report by the Insurance Institute for Highway Safety ( IIHS ), optimization based on biomechanical data can reduce the risk of collision injuries 12% above.

2.2  Aerospace Field

In the aerospace field, human motion biomechanics analysis is mainly used for cabin interface design and astronaut operation optimization. Due to the special nature of the space environment (such as microgravity conditions), the operational efficiency and safety of astronauts are directly related to mission success or failure. In recent years, NASA and the European Space Agency ( ESA ) have successively carried out biomechanics research based on motion capture to simulate astronauts' movement patterns inside the cabin. For example, ESA in 2023 a study published in AnyBody the year, an inertial motion capture system was used to record astronauts' movement trajectories in a simulated weightless environment, and software calculated the stress on the lumbar spine and knee joints under different working postures. The study found that optimizing the layout of cabin handles could reduce lumbar load during astronaut operations by about 20%[3]. Additionally, combined with artificial intelligence algorithms, researchers developed an adaptive human-machine interaction system that can dynamically adjust the interface layout based on astronauts' real-time biomechanical data, further improving operational efficiency.

🔥  3  Technical Challenges and Future Directions

Although motion capture and biomechanics analysis have made significant progress in industrial applications, some technical challenges remain. First, the fusion processing of multimodal data (such as synchronization of optical and inertial data) requires more efficient algorithm support. Second, the generalization ability of models in complex environments (such as microgravity, high temperature, and other extreme conditions) still needs further research. In addition, the lack of interpretability of artificial intelligence models also limits their application in safety-critical scenarios.

In the future, with the miniaturization of sensor technology and continuous optimization of artificial intelligence algorithms, human motion biomechanics analysis will be more deeply integrated into various aspects of industrial design. The rise of digital twin technology is particularly noteworthy: by constructing high-fidelity digital human models, designers can predict and optimize human-machine interaction schemes in virtual environments, thereby reducing the cost and time of physical prototype testing.

🔥 Conclusion

The combination of artificial intelligence and human motion biomechanics analysis has brought new opportunities to the field of industrial intelligent manufacturing. The combination of motion capture systems and biomechanics software not only provides rich data support but also improves the efficiency and safety of human-machine interaction design through intelligent analysis. In fields such as automotive and aerospace, this technology has become an important tool for optimizing product design and enhancing user experience. In the future, with further technological maturity, its application scope is expected to expand to more industrial scenarios.